Supported models offer a semantics for logic programs that relaxes the minimality constraint of stable models while maintaining logical consistency through a support condition. Despite their theoretical significance since 1988, supported models lack practical computational tools integrated with modern Answer Set Programming (ASP) infrastructure. We present a transformation-based method that enables computation of supported models using standard stable model solvers. Our approach transforms any ground logic program into an equivalent program whose stable models correspond exactly to the supported models of the original program. We implement this transformation as a preprocessor for Clingo and demonstrate applications in software verification, medical diagnosis, and planning where supported models enable valuable exploratory reasoning beyond stable models.